论文标题
加权危害比估计延迟和减少治疗效果
Weighted hazard ratio estimation for delayed and diminishing treatment effect
论文作者
论文摘要
在验证性临床试验中，已经观察到了未达到事件结果的验证性临床试验中的非比例危害（NPH）。在NPH下，随着时间的推移，危险比不会保持恒定，并且对数秩检验不再是最强大的测试。加权对数秩检验（WLRT）已引入以处理非比例性的存在。我们将注意力集中在WLRT和互补的COX模型上，该模型基于Lin和León（2017）提出的时变治疗效果（DOI：10.1016/j.conctc.2017.09.004）。我们将调查提出的加权危害比（WHR）方法是否在WLRT统计量最强大的测试的情况下是公正的。在WLRT统计量最佳的治疗效果方案中，COX模型估计的随时间变化的治疗效应估计了治疗效果非常接近True。但是，当真实危害比很大时，我们注意到，提出的模型会随着时间的推移高估治疗效果和治疗概况。但是，在延迟的治疗情况下，随着时间的推移，估计的治疗效果概况通常接近真实概况。对于这两种情况，我们都在分析上证明了危险比函数在某些限制下大致相等。总之，我们的结果表明，在某些情况下，给定的WLRT将是最强大的，我们观察到来自相应的COX模型的WHR估计了接近真正的COX的治疗效果。
Non-proportional hazards (NPH) have been observed in confirmatory clinical trials with time to event outcomes. Under NPH, the hazard ratio does not stay constant over time and the log-rank test is no longer the most powerful test. The weighted log-rank test (WLRT) has been introduced to deal with the presence of non-proportionality. We focus our attention on the WLRT and the complementary Cox model based on the time-varying treatment effect proposed by Lin and León (2017) (doi: 10.1016/j.conctc.2017.09.004). We will investigate whether the proposed weighted hazard ratio (WHR) approach is unbiased in scenarios where the WLRT statistic is the most powerful test. In the diminishing treatment effect scenario where the WLRT statistic would be most optimal, the time-varying treatment effect estimated by the Cox model estimates the treatment effect very close to the true one. However, when the true hazard ratio is large we note that the proposed model overestimates the treatment effect and the treatment profile over time. However, in the delayed treatment scenario, the estimated treatment effect profile over time is typically close to the true profile. For both scenarios, we have demonstrated analytically that the hazard ratio functions are approximately equal under certain constraints. In conclusion, our results demonstrate that in certain scenarios where a given WLRT would be most powerful, we observe that the WHR from the corresponding Cox model is estimating the treatment effect close to the true one.
